"""
仅推理 备份
"""

"""******************************* PlotDub 软件初始化 ************************************"""

import os, sys, json, shutil

# 获取当前目录并存入环境变量
now_dir = os.getcwd()
sys.path.insert(0, now_dir)

# 创建缓存文件夹，清理无用缓存文件
tmp = os.path.join(now_dir, "TEMP")
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
if os.path.exists(tmp):
    for name in os.listdir(tmp):
        if name == "jieba.cache": continue
        path = "%s/%s"%(tmp, name)
        delete = os.remove if os.path.isfile(path) else shutil.rmtree
        try:
            delete(path)
        except Exception as e:
            print(str(e))
            pass


from tools.i18n.i18n import I18nAuto, scan_language_list
import torch


# 设置语言类型环境变量，创建国际化（i18n）的类对象
language = "Auto"
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
os.environ["language"] = language
i18n = I18nAuto(language=language)

# pytorch 硬件检查与设置
# 检查显卡
if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"
# 设置 PyTorch 的随机种子，使得实验结果具有可重复性
torch.manual_seed(233333)
# 读取硬件配置，设置 GPU 参数
ngpu = torch.cuda.device_count()  # 返回当前系统中可用的 GPU 数量
gpus = "-".join([str(i) for i in range(ngpu)]) if ngpu > 0 else "CPU"
# CUDA设备的设置和判断是否使用半精度浮点数
if "_CUDA_VISIBLE_DEVICES" in os.environ:
    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()

"""******************************* GSV 模型加载部分 **************************************"""
# 根据输入参数确定版本
# 如果没有传入命令行参数，则默认将 'v2' 追加到 sys.argv 中
if len(sys.argv) == 1:
    sys.argv.append('v2')
# 根据命令行参数设置版本号，如果第一个参数是 "v1" 则版本为 "v1"，否则为 "v2"
version = "v1" if sys.argv[1] == "v1" else "v2"
# 设置环境变量
os.environ["version"] = version

# 语言字典
dict_language_v1 = {
    i18n("中文"): "all_zh",  # 全部按中文识别
    i18n("英文"): "en",  # 全部按英文识别#######不变
    i18n("日文"): "all_ja",  # 全部按日文识别
    i18n("中英混合"): "zh",  # 按中英混合识别####不变
    i18n("日英混合"): "ja",  # 按日英混合识别####不变
    i18n("多语种混合"): "auto",  # 多语种启动切分识别语种
}
dict_language_v2 = {
    i18n("中文"): "all_zh",  # 全部按中文识别
    i18n("英文"): "en",  # 全部按英文识别#######不变
    i18n("日文"): "all_ja",  # 全部按日文识别
    i18n("粤语"): "all_yue",  # 全部按中文识别
    i18n("韩文"): "all_ko",  # 全部按韩文识别
    i18n("中英混合"): "zh",  # 按中英混合识别####不变
    i18n("日英混合"): "ja",  # 按日英混合识别####不变
    i18n("粤英混合"): "yue",  # 按粤英混合识别####不变
    i18n("韩英混合"): "ko",  # 按韩英混合识别####不变
    i18n("多语种混合"): "auto",  # 多语种启动切分识别语种
    i18n("多语种混合(粤语)"): "auto_yue",  # 多语种启动切分识别语种
}
dict_language = dict_language_v1 if version == 'v1' else dict_language_v2

# SOVIT 和 GPT 预训练模型的位置（底模）
pretrained_gpt_name = [
    "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
    "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"]
pretrained_sovits_name = ["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
                          "GPT_SoVITS/pretrained_models/s2G488k.pth"]

# 确认预训练模型文件是否存在，并重新组织路径列表
_ = [[], []]
for i in range(2):
    if os.path.exists(pretrained_gpt_name[i]):
        _[0].append(pretrained_gpt_name[i])
    if os.path.exists(pretrained_sovits_name[i]):
        _[-1].append(pretrained_sovits_name[i])
pretrained_gpt_name, pretrained_sovits_name = _
# print(pretrained_gpt_name)
# print(pretrained_sovits_name)

if not pretrained_gpt_name: print("未在 GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/ 下找到.ckpt预训练模型文件")
if not pretrained_sovits_name: print(
    "未在 GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/ 下找到.pth预训练模型文件")

# 检查是否存在角色列表+权重路径配置文件，如果不存在则创建默认模板
if os.path.exists(f"./roles.json"):
    pass
else:
    with open(f"./roles.json", 'w', encoding="utf-8") as file:
        json.dump({'GenshinImpact': {}, 'StarRail': {}}, file)


# 定义一个类，表示角色的信息
class Role:
    def __init__(self, role_name, gpt_path, sovits_path):
        self.role_name = role_name
        self.gpt_path = gpt_path
        self.sovits_path = sovits_path

    def __repr__(self):
        return f"Role(role_name={self.role_name}, gpt_path={self.gpt_path}, sovits_path={self.sovits_path})\n"


# 读取角色模型权重列表
genshin_impact_dict = {}  # 用于存储 GenshinImpact 角色信息的哈希表
star_rail_dict = {}      # 用于存储 StarRail 角色信息的哈希表

with open(f"./roles.json", 'r', encoding="utf-8") as file:
    roles_data = json.load(file)  # 解析 JSON 文件为字典

    # 从 "GenshinImpact" 中获取角色及路径信息并构建哈希表
    genshin_impact_data = roles_data.get("GenshinImpact", {})
    for role, paths in genshin_impact_data.items():
        gpt_path = paths.get("GPT", "")
        if gpt_path == "": gpt_path = pretrained_gpt_name[0] if len(pretrained_gpt_name) >= 1 else ""
        sovits_path = paths.get("SoVITS", "")
        if sovits_path == "": sovits_path = pretrained_sovits_name[0] if len(pretrained_sovits_name) >= 1 else ""
        role_instance = Role(role_name=role, gpt_path=gpt_path, sovits_path=sovits_path)
        genshin_impact_dict[role] = role_instance  # 将角色名字作为键，Role实例作为值

    # 从 "StarRail" 中获取角色及路径信息并构建哈希表
    star_rail_data = roles_data.get("StarRail", {})
    for role, paths in star_rail_data.items():
        gpt_path = paths.get("GPT", "")
        if gpt_path == "": gpt_path = pretrained_gpt_name[0] if len(pretrained_gpt_name) >= 1 else ""
        sovits_path = paths.get("SoVITS", "")
        if sovits_path == "": sovits_path = pretrained_sovits_name[0] if len(pretrained_sovits_name) >= 1 else ""
        role_instance = Role(role_name=role, gpt_path=gpt_path, sovits_path=sovits_path)
        star_rail_dict[role] = role_instance  # 将角色名字作为键，Role实例作为值

# 打印结果
print("角色模型列表加载成功：")
print("Genshin Impact List:\n", genshin_impact_dict)
print("Star Rail List:\n", star_rail_dict)
print("\n")

"""******************* SoVITS 模型加载部分 *********************"""
from module.models import SynthesizerTrn


class DictToAttrRecursive(dict):
    def __init__(self, input_dict):
        super().__init__(input_dict)
        for key, value in input_dict.items():
            if isinstance(value, dict):
                value = DictToAttrRecursive(value)
            self[key] = value
            setattr(self, key, value)

    def __getattr__(self, item):
        try:
            return self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")

    def __setattr__(self, key, value):
        if isinstance(value, dict):
            value = DictToAttrRecursive(value)
        super(DictToAttrRecursive, self).__setitem__(key, value)
        super().__setattr__(key, value)

    def __delattr__(self, item):
        try:
            del self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")


def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
    global vq_model, hps, version, dict_language
    dict_s2 = torch.load(sovits_path, map_location="cpu")
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
        hps.model.version = "v1"
    else:
        hps.model.version = "v2"
    version = hps.model.version
    # print("sovits版本:",hps.model.version)
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model
    )
    if ("pretrained" not in sovits_path):
        del vq_model.enc_q
    if is_half == True:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
    dict_language = dict_language_v1 if version =='v1' else dict_language_v2
    with open("./weight.json")as f:
        data=f.read()
        data=json.loads(data)
        data["SoVITS"][version]=sovits_path
    with open("./weight.json","w")as f:f.write(json.dumps(data))
    if prompt_language is not None and text_language is not None:
        if prompt_language in list(dict_language.keys()):
            prompt_text_update, prompt_language_update = {'__type__':'update'},  {'__type__':'update', 'value':prompt_language}
        else:
            prompt_text_update = {'__type__':'update', 'value':''}
            prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
        if text_language in list(dict_language.keys()):
            text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
        else:
            text_update = {'__type__':'update', 'value':''}
            text_language_update = {'__type__':'update', 'value':i18n("中文")}
        return  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update

change_sovits_weights(genshin_impact_dict["派蒙"].sovits_path)

"""********************************************************"""


"""******************* GPT 模型加载部分 *********************"""
from AR.models.t2s_lightning_module import Text2SemanticLightningModule


def change_gpt_weights(gpt_path):
    global hz, max_sec, t2s_model, config
    hz = 50
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    max_sec = config["data"]["max_sec"]
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    if is_half == True:
        t2s_model = t2s_model.half()
    t2s_model = t2s_model.to(device)
    t2s_model.eval()
    total = sum([param.nelement() for param in t2s_model.parameters()])
    print("Number of parameter: %.2fM" % (total / 1e6))
    with open("./weight.json")as f:
        data=f.read()
        data=json.loads(data)
        data["GPT"][version]=gpt_path
    with open("./weight.json","w")as f:f.write(json.dumps(data))

change_gpt_weights(genshin_impact_dict["派蒙"].gpt_path)


"""********************************************************"""


# 加载其他必要的模型路径配置（如语义模型、语音特征提取模型等）
# 加载 ssl 模型
cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
from feature_extractor import cnhubert

cnhubert.cnhubert_base_path = cnhubert_base_path
ssl_model = cnhubert.get_model()
if is_half:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)
# 加载 bert 模型
bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


# 功能函数
def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    return phone_level_feature.T


def get_bert_inf(phones, word2ph, norm_text, language):
    language = language.replace("all_", "")
    if language == "zh":
        bert = get_bert_feature(norm_text, word2ph).to(device)  # .to(dtype)
    else:
        bert = torch.zeros(
            (1024, len(phones)),
            dtype=torch.float16 if is_half == True else torch.float32,
        ).to(device)

    return bert


from text import cleaned_text_to_sequence
from text.cleaner import clean_text
def clean_text_inf(text, language, version):
    phones, word2ph, norm_text = clean_text(text, language, version)
    phones = cleaned_text_to_sequence(phones, version)
    return phones, word2ph, norm_text


from text import chinese
import LangSegment, re
dtype = torch.float16 if is_half else torch.float32
def get_phones_and_bert(text, language, version, final=False):
    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
        language = language.replace("all_", "")
        if language == "en":
            LangSegment.setfilters(["en"])
            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
        else:
            # 因无法区别中日韩文汉字,以用户输入为准
            formattext = text
        while "  " in formattext:
            formattext = formattext.replace("  ", " ")
        if language == "zh":
            if re.search(r'[A-Za-z]', formattext):
                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
                formattext = chinese.mix_text_normalize(formattext)
                return get_phones_and_bert(formattext, "zh", version)
            else:
                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
                bert = get_bert_feature(norm_text, word2ph).to(device)
        elif language == "yue" and re.search(r'[A-Za-z]', formattext):
            formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
            formattext = chinese.mix_text_normalize(formattext)
            return get_phones_and_bert(formattext, "yue", version)
        else:
            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
            bert = torch.zeros(
                (1024, len(phones)),
                dtype=torch.float16 if is_half == True else torch.float32,
            ).to(device)
    elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
        textlist = []
        langlist = []
        LangSegment.setfilters(["zh", "ja", "en", "ko"])
        if language == "auto":
            for tmp in LangSegment.getTexts(text):
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        elif language == "auto_yue":
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "zh":
                    tmp["lang"] = "yue"
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        else:
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "en":
                    langlist.append(tmp["lang"])
                else:
                    # 因无法区别中日韩文汉字,以用户输入为准
                    langlist.append(language)
                textlist.append(tmp["text"])
        print(textlist)
        print(langlist)
        phones_list = []
        bert_list = []
        norm_text_list = []
        for i in range(len(textlist)):
            lang = langlist[i]
            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
            bert = get_bert_inf(phones, word2ph, norm_text, lang)
            phones_list.append(phones)
            norm_text_list.append(norm_text)
            bert_list.append(bert)
        bert = torch.cat(bert_list, dim=1)
        phones = sum(phones_list, [])
        norm_text = ''.join(norm_text_list)

    if not final and len(phones) < 6:
        return get_phones_and_bert("." + text, language, version, final=True)

    return phones, bert.to(dtype), norm_text


"""******************************* GSV 模型推理部分 **************************************"""
# 文本处理函数
# split cut 函数，用于推理语句切分
# 定义标点符号，用于文本切分处理
punctuation = {'!', '?', '…', ',', '.', '-', " "}


def change_role(new_role):
    global current_role
    gpt_path = genshin_impact_dict[new_role].gpt_path
    print(gpt_path)
    sovits_path = genshin_impact_dict[new_role].sovits_path
    print(sovits_path)
    change_sovits_weights(sovits_path, "中文", "中文")
    change_gpt_weights(gpt_path)
    current_role = new_role

def change_emotion(new_meo):
    global current_emo
    inp_ref = load_audio("D:/Project\Python/GPT-SoVITS/模型参数\派蒙/生气-呜哇好生气啊！不要把我跟一斗相提并论！.wav",
                         int(hps.data.sampling_rate))
    prompt_text = "呜哇好生气啊！不要把我跟一斗相提并论！"
    current_emo = new_meo
    return inp_ref, prompt_text

def split(todo_text):
    todo_text = todo_text.replace("……", "。").replace("——", "，")
    if todo_text[-1] not in splits:
        todo_text += "。"
    i_split_head = i_split_tail = 0
    len_text = len(todo_text)
    todo_texts = []
    while 1:
        if i_split_head >= len_text:
            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
        if todo_text[i_split_head] in splits:
            i_split_head += 1
            todo_texts.append(todo_text[i_split_tail:i_split_head])
            i_split_tail = i_split_head
        else:
            i_split_head += 1
    return todo_texts


def cut1(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    split_idx = list(range(0, len(inps), 4))
    split_idx[-1] = None
    if len(split_idx) > 1:
        opts = []
        for idx in range(len(split_idx) - 1):
            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
    else:
        opts = [inp]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut2(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    if len(inps) < 2:
        return inp
    opts = []
    summ = 0
    tmp_str = ""
    for i in range(len(inps)):
        summ += len(inps[i])
        tmp_str += inps[i]
        if summ > 50:
            summ = 0
            opts.append(tmp_str)
            tmp_str = ""
    if tmp_str != "":
        opts.append(tmp_str)
    # print(opts)
    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
        opts[-2] = opts[-2] + opts[-1]
        opts = opts[:-1]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut3(inp):
    inp = inp.strip("\n")
    opts = ["%s" % item for item in inp.strip("。").split("。")]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut4(inp):
    inp = inp.strip("\n")
    opts = ["%s" % item for item in inp.strip(".").split(".")]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut5(inp):
    inp = inp.strip("\n")
    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
    mergeitems = []
    items = []

    for i, char in enumerate(inp):
        if char in punds:
            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
                items.append(char)
            else:
                items.append(char)
                mergeitems.append("".join(items))
                items = []
        else:
            items.append(char)

    if items:
        mergeitems.append("".join(items))

    opt = [item for item in mergeitems if not set(item).issubset(punds)]
    return "\n".join(opt)


# 文本处理
def process_text(texts):
    _text = []
    if all(text in [None, " ", "\n", ""] for text in texts):
        raise ValueError(i18n("请输入有效文本"))
    for text in texts:
        if text in [None, " ", ""]:
            pass
        else:
            _text.append(text)
    return _text


# 短文本合并
def merge_short_text_in_array(texts, threshold):
    if (len(texts)) < 2:
        return texts
    result = []
    text = ""
    for ele in texts:
        text += ele
        if len(text) >= threshold:
            result.append(text)
            text = ""
    if (len(text) > 0):
        if len(result) == 0:
            result.append(text)
        else:
            result[len(result) - 1] += text
    return result


from tools.my_utils import load_audio
from module.mel_processing import spectrogram_torch
def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    maxx=audio.abs().max()
    if(maxx>1):audio/=min(2,maxx)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(
        audio_norm,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    return spec


# 用于语句切分
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }

# 推理函数
from time import time as ttime
import numpy as np
import librosa
import traceback

cache = {}


def get_tts_wav(
        # ref_wav_path,  # 参考音频路径，用于提供目标音色的参考
        # prompt_text,  # 参考文本，需与参考音频内容相关，用于语义建模
        # prompt_language,  # 参考文本语言
        text,  # 目标文本，用户希望生成语音的内容
        # text_language,  # 目标文本语言
        # how_to_cut=i18n("不切"),  # 文本切分方式（如按标点符号、固定字数切分）
        top_k=20,  # Top-k 采样参数，控制生成多样性
        top_p=0.6,  # Top-p 采样参数，控制生成多样性
        temperature=0.6,  # 生成温度参数，调整输出的随机性
        # ref_free=False,  # 是否启用参考自由模式，若为 True 则不使用参考音频和文本
        speed=1,  # 生成音频的播放速度调整系数
        if_freeze=False,  # 是否启用冻结模式，缓存中间结果以复用
        inp_refs=None  # 额外参考音频列表，用于优化生成的音色
):
    """
        根据参考音频、参考文本和目标文本生成语音。

        参数：
            ref_wav_path (str): 参考音频路径，用于提供音色参考。
            prompt_text (str): 参考文本，与参考音频内容相关。
            prompt_language (str): 参考文本语言（如 "zh" 或 "en"）。
            text (str): 需要生成语音的目标文本。
            text_language (str): 目标文本语言（如 "zh" 或 "en"）。
            how_to_cut (str): 文本切分方式，可选值为预定义策略（如按标点切分）。
            top_k (int): Top-k 采样参数，控制生成多样性。
            top_p (float): Top-p 采样参数，控制生成多样性。
            temperature (float): 生成温度参数，控制输出随机性。
            ref_free (bool): 是否启用参考自由模式，若为 True 则忽略参考音频和文本。
            speed (float): 调整生成语音的播放速度。
            if_freeze (bool): 是否启用冻结模式，复用缓存中的生成结果。
            inp_refs (list): 额外的参考音频列表，用于优化音色。

        返回：
            generator: 以采样率和语音数据形式返回生成的语音。
    """
    ref_wav_path = "D:/Project\Python/GPT-SoVITS/模型参数\派蒙/生气-呜哇好生气啊！不要把我跟一斗相提并论！.wav"
    prompt_text = "呜哇好生气啊！不要把我跟一斗相提并论！"
    prompt_language = "中文"
    text_language = "中文"
    how_to_cut = i18n("凑四句一切")
    ref_free = False

    global cache
    # 检查输入合法性
    if not ref_wav_path:
        gr.Warning(i18n('请上传参考音频'))
    if not text:
        gr.Warning(i18n('请填入推理文本'))

    # 时间记录列表，用于性能统计
    t = []

    # 如果未提供参考文本，则进入参考自由模式
    if prompt_text is None or len(prompt_text) == 0:
        ref_free = True

    # 记录起始时间
    t0 = ttime()

    # 将语言映射为模型支持的格式
    prompt_language = dict_language[prompt_language]
    text_language = dict_language[text_language]

    # 如果不在参考自由模式下，加载并处理参考音频
    if not ref_free:
        prompt_text = prompt_text.strip("\n")
        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
        print(i18n("实际输入的参考文本:"), prompt_text)

    text = text.strip("\n")  # 去除目标文本的换行符
    print(i18n("实际输入的目标文本:"), text)

    zero_wav = np.zeros(
        int(hps.data.sampling_rate * 0.3),
        dtype=np.float16 if is_half == True else np.float32,
    )

    # 加载参考音频并检查长度
    if not ref_free:
        with torch.no_grad():
            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
                gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))

            # 转换为 Torch Tensor 并添加额外的静音部分
            wav16k = torch.from_numpy(wav16k).to(device)
            zero_wav_torch = torch.from_numpy(zero_wav)
            if is_half:
                wav16k = wav16k.half().to(device)
                zero_wav_torch = zero_wav_torch.half().to(device)
            else:
                wav16k = wav16k.to(device)
                zero_wav_torch = zero_wav_torch.to(device)
            wav16k = torch.cat([wav16k, zero_wav_torch])

            # 提取 SSL 模型的语义特征
            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
                "last_hidden_state"
            ].transpose(
                1, 2
            )
            codes = vq_model.extract_latent(ssl_content)
            prompt_semantic = codes[0, 0]
            prompt = prompt_semantic.unsqueeze(0).to(device)

    t1 = ttime()  # 时间记录
    t.append(t1 - t0)  # 记录第一阶段耗时

    # 根据切分策略对目标文本进行切分
    if how_to_cut == i18n("凑四句一切"):
        text = cut1(text)
    elif how_to_cut == i18n("凑50字一切"):
        text = cut2(text)
    elif how_to_cut == i18n("按中文句号。切"):
        text = cut3(text)
    elif how_to_cut == i18n("按英文句号.切"):
        text = cut4(text)
    elif how_to_cut == i18n("按标点符号切"):
        text = cut5(text)

    # 进一步清理文本切分结果
    while "\n\n" in text:
        text = text.replace("\n\n", "\n")
    print(i18n("实际输入的目标文本(切句后):"), text)

    texts = text.split("\n")  # 切分为句子
    texts = process_text(texts)  # 进一步处理文本
    texts = merge_short_text_in_array(texts, 5)  # 合并过短的句子

    audio_opt = []  # 保存生成的音频结果

    # 提取参考文本的特征
    if not ref_free:
        phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)

    # 遍历切分后的每个目标句子，逐句生成语音
    for i_text, text in enumerate(texts):
        # 解决输入目标文本的空行导致报错的问题
        if (len(text.strip()) == 0):
            continue

        # 输出当前合成的目标语句
        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
        print(i18n("实际输入的目标文本(每句):"), text)

        # 提取目标文本的特征
        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
        print(i18n("前端处理后的文本(每句):"), norm_text2)

        # 根据是否参考自由模式决定如何合并参考和目标特征
        if not ref_free:
            bert = torch.cat([bert1, bert2], 1)
            all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
        else:
            bert = bert2
            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)

        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)

        t2 = ttime()  # 记录推理开始时间

        # 检查缓存中是否已有语义特征
        if (i_text in cache and if_freeze == True):
            pred_semantic = cache[i_text]
        else:
            # 调用语义生成模型
            with torch.no_grad():
                pred_semantic, idx = t2s_model.model.infer_panel(
                    all_phoneme_ids,
                    all_phoneme_len,
                    None if ref_free else prompt,
                    bert,
                    # prompt_phone_len=ph_offset,
                    top_k=top_k,
                    top_p=top_p,
                    temperature=temperature,
                    early_stop_num=hz * max_sec,
                )
                pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
                cache[i_text] = pred_semantic

        t3 = ttime()  # 推理结束时间

        # 加载参考音频或额外参考音频用于解码
        refers = []
        if inp_refs:
            for path in inp_refs:
                try:
                    refer = get_spepc(hps, path.name).to(dtype).to(device)
                    refers.append(refer)
                except:
                    traceback.print_exc()
        if (len(refers) == 0): refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]

        # 解码生成音频
        audio = vq_model.decode(
            pred_semantic,
            torch.LongTensor(phones2).to(device).unsqueeze(0),
            refers,
            speed=speed
        ).detach().cpu().numpy()[0, 0]

        # 防止爆音
        max_audio = np.abs(audio).max()  # 简单防止16bit爆音
        if max_audio > 1: audio /= max_audio

        # 添加静音部分
        audio_opt.append(audio)
        audio_opt.append(zero_wav)

        t4 = ttime()  # 解码结束时间
        t.extend([t2 - t1, t3 - t2, t4 - t3])
        t1 = ttime()  # 刷新 t1

    # 打印时间统计
    print("%.3f\t%.3f\t%.3f\t%.3f" %
          (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))
          )

    # 输出最终音频
    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
        np.int16
    )


# webui
try:
    import gradio.analytics as analytics
    analytics.version_check = lambda: None
except:
    ...
import gradio as gr

infer_ttswebui = 9872
infer_ttswebui = int(infer_ttswebui)
is_share = "False"
is_share = eval(is_share)

from pypinyin import pinyin, lazy_pinyin


def get_sorted_role_names(role_dict):
    # 提取字典中的所有角色名字并根据拼音排序
    role_names = list(role_dict.keys())  # 获取所有角色名字
    # 使用 lazy_pinyin() 将每个名字转换为拼音并排序
    sorted_role_names = sorted(role_names, key=lambda name: ''.join(lazy_pinyin(name)))
    return sorted_role_names


def html_center(text, label='p'):
    return f"""<div style="text-align: center; margin: 100; padding: 50;">
                <{label} style="margin: 0; padding: 0;">{text}</{label}>
                </div>"""


def html_left(text, label='p'):
    return f"""<div style="text-align: left; margin: 0; padding: 0;">
                <{label} style="margin: 0; padding: 0;">{text}</{label}>
                </div>"""


def html_center_all(text, label='p'):
    return f"""<div style="display: flex; justify-content: center; align-items: center; min-height: 5vh; padding: 5;">
                <{label} style="margin: 0; padding: 0;">{text}</{label}>
               </div>"""


# 提取角色名称并按拼音排序
genshin_impact_role_names = get_sorted_role_names(genshin_impact_dict)
star_rail_role_names = get_sorted_role_names(star_rail_dict)

# 情感列表
emotion_list = [
    "平静",
    "开心",
    "生气"
]

# def load_ref_audio():
#     global inp_ref, prompt_text
#     inp_ref = load_audio("D:/Project\Python/GPT-SoVITS/模型参数\派蒙/生气-呜哇好生气啊！不要把我跟一斗相提并论！.wav",int(hps.data.sampling_rate))
#     prompt_text = gr.State("呜哇好生气啊！不要把我跟一斗相提并论！")
#
#
# load_ref_audio()

def button_click(input_ref, ref_text):
    print(f"Received input_ref: {input_ref}, ref_text: {ref_text}")

with gr.Blocks(title="PlotDub WebUI") as app:
    gr.Markdown(html_center_all(i18n("原神剧情朗读V0.0"), 'h1'))
    # 软件说明
    gr.Markdown(
        value=i18n(
            "本软件基于 GPT-SoVITS2 开发. <br>本软件继承 GPT-SoVITS2 开源协议.")
    )
    with gr.Group():
        gr.Markdown(html_center_all(i18n("角色&情感"), 'h4'))
        with gr.Row():
            # 角色选择框
            with gr.Column(scale=13):
                Role_dropdown = gr.Dropdown(
                    label="原神角色选择",
                    choices=genshin_impact_role_names,  # 按拼音排序
                    value="",
                    interactive=True,
                    scale=14
                )
                # refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
                # refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
            # 情感选择框
            with gr.Column(scale=13):
                Emo_dropdown = gr.Dropdown(
                    label="情感选择",
                    choices=emotion_list,
                    value="",
                    interactive=True,
                    scale=14
                )
        gr.Markdown(html_center_all(i18n("输入"), 'h4'))
        with gr.Row():
            text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=10, max_lines=26)
        gr.Markdown(html_center_all(i18n("合成"), 'h4'))
        with gr.Row():
            with gr.Column():
                if_freeze = gr.Checkbox(label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"), value=False,
                                        interactive=True, show_label=True, scale=1)
                speed = gr.Slider(minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True,
                                  scale=1)
                gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
                top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True,
                                  scale=1)
                top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True,
                                  scale=1)
                temperature = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1,
                                        interactive=True, scale=1)
            inference_button = gr.Button(i18n("合成语音"), variant="primary", size='sm', scale=1)
            output = gr.Audio(label=i18n("输出的语音"), scale=14)

        inference_button.click(
            get_tts_wav,
            [text, top_k, top_p, temperature,
             speed, if_freeze],
            [output],
        )
        # inference_button.click(
        #     button_click,
        #     [inp_ref, prompt_text],
        #     [],
        # )
        Role_dropdown.change(
            change_role,
            [Role_dropdown],
            []
        )
        # Emo_dropdown.change(
        #     change_emotion,
        #     [Emo_dropdown],
        #     [inp_ref, prompt_text]
        # )



if __name__ == '__main__':
    app.queue().launch(  # concurrency_count=511, max_size=1022
        server_name="0.0.0.0",
        inbrowser=True,
        share=is_share,
        server_port=infer_ttswebui,
        quiet=True,
    )
